Recently, high quality three-dimensional images have been used in image diagnosis with the advancement of medical equipment (e.g., multidetector CT and the like). As a three-dimensional image is formed of a multiple of two-dimensional images and has a large amount of information, the doctor may sometimes require a prolonged time to find out a desired observation region and give a diagnosis. Consequently, it is practiced to extract an organ of interest and perform NIP, VR, or CPR display, or the like, in order to enhance the visibility of an entire organ or a lesion and improve efficiency of diagnosis.
In the meantime, as a method of extracting a blood vessel or a bone in a medical image, Hessian analysis using a Hessian matrix is proposed (refer to A. F. Frangi et al., “Multiscale vessel enhancement filtering”, Proceedings of MICCAI, Vol. 1496, pp. 130-137, 1998). The Hessian analysis discriminates whether a local structure in an image is a point, a line, or a plane by analyzing eigenvalues of a Hessian matrix whose elements are second order partial differential coefficients calculated by the use of the second differential kernel of a given filter, such as Gaussian kernel or the like. The use of the Hessian analysis allows a blood vessel and a bone to be discriminated as a line-like structure and a plate-like structure respectively.
There are cases, however, in which, if another structure is present in the vicinity of a line-like structure (vicinity structure), the method of A. F. Frangi et al., “Multiscale vessel enhancement filtering”, Proceedings of MICCAI, Vol. 1496, pp. 130-137, 1998 erroneously discriminates the vicinity structure as the line-like structure. The method of M. Law and A. Chung, “Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux”, Proceedings of ECCV, pp. 368-382, 2008 improves the filter proposed in A. F. Frangi et al., “Multiscale vessel enhancement filtering”, Proceedings of MICCAI, Vol. 1496, pp. 130-137, 1998 by convoluting a function representing a solid sphere (solid sphere model function) with the inside of the spherical shape as 1 and the outside as 0 and limiting the calculation range of the second order partial differential coefficients to the surface of the sphere in Hessian analysis, whereby the influence of the vicinity structure on the filtering result may be reduced.